This project is an extension of previous work involving the creation, development and testing of image-processing techniques designed to improve diagnostic performance. Current work has centered on methods relevant to the processes of radiologic image subtraction and tomosynthesis. Work continues in the area of automatic manipulation to facilitate registration. Particular emphasis has been placed on: 1) methods for automatically segmenting radiographic images into regions of diagnostic interest, in anticipation of the automated detection of associated lesions, 2) methods for quantifying the apparent size of lesions from these images, 3) methods for increasing the efficiency of complex, spatial-frequency-dependent manipulations essential for optimization of diagnostic performance of specific tasks. The recognition and delineation of areas showing trabecular bone was set as a primary target because of its importance in the diagnosis and monitoring of periodontal and other lytic bone diseases. Efforts continue in quad-tree image characterization using split-and-merge procedures for image segmentation. Second-order statistics and scale-invariant transformations are also being investigated in this context in order to increase the specificity of the segmentation process. Other work involves the use "shaded aperture" sampling techniques to eliminate "ringing" artifacts associated with high- pass filtered tomosynthetic reconstructions. Recent findings show that optimum weighting of 25 projections permits suppression of the first side lobe of the transfer function to about 1%. Future activity will continue coordinate image-processing efforts with research directed toward the development of complete diagnostic system.